class pytorch_nn():
def __init__(self):
weights_pth = os.path.join('./checkpoints', 'nn_weights.pth')
self.model = MLP(input_size=5, output_size=3)
self.model.load_state_dict(
torch.load(weights_pth, map_location=torch.device('cpu')))
self.model.eval()
def predict(self, in_vector):
in_vector = torch.from_numpy(np.array(in_vector))
in_vector = Variable(in_vector).float()
outputs = self.model.forward(in_vector)
prob, pred = outputs.max(0, keepdim=True)
return outputs, prob, pred
def getNewNN(thisDict):
weights_pth = "checkpoints/nn_weights.pth"
model = MLP(input_size=5, output_size=3)
model.load_state_dict(
torch.load(weights_pth, map_location=torch.device('cpu')))
model.eval()
in_vector = []
for key, value in thisDict.items():
print(key, value)
temp = masterMerged.loc[masterMerged[key + "_x"] == value].head(1)
in_vector.append(temp.iloc[0][key + "_y"])
#in_vector = [1, 589, 9, 1, 0]
in_vector = torch.from_numpy(np.array(in_vector))
in_vector = Variable(in_vector).float()
outputs = model.forward(in_vector)
return outputs.tolist()
class VisualizationDemoMLP(object):
def __init__(self,
cfg_object,
cfg_keypoint,
instance_mode=ColorMode.IMAGE):
"""
Args:
cfg (CfgNode):
instance_mode (ColorMode):
parallel (bool): whether to run the model in different processes from visualization.
Useful since the visualization logic can be slow.
"""
self.metadata_object = MetadataCatalog.get("__unused")
self.metadata_keypoint = MetadataCatalog.get(
cfg_keypoint.DATASETS.TEST[0] if len(cfg_keypoint.DATASETS.TEST
) else "__unused")
self.cpu_device = torch.device("cpu")
self.instance_mode = instance_mode
self.predictor_object = DefaultPredictor(cfg_object)
self.predictor_keypoint = DefaultPredictor(cfg_keypoint)
self.head_pose_module = module_init(cfg_keypoint)
self.mtcnn = MTCNN()
self.transformations = transforms.Compose([transforms.Resize(224), \
transforms.CenterCrop(224), transforms.ToTensor(), \
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
self.softmax = nn.Softmax(dim=1).cuda()
idx_tensor = [idx for idx in range(66)]
self.idx_tensor = torch.FloatTensor(idx_tensor).cuda()
self.data_json = {}
self.data_json['object_detection'] = {}
self.data_json['keypoint_detection'] = {}
self.data_json['head_pose_estimation'] = {}
self.frame_count = 0
self.mlp_model = MLP(input_size=26, output_size=1).cuda()
self.mlp_model.load_state_dict(torch.load(cfg_keypoint.MLP.PRETRAINED))
self.mlp_model.eval()
def run_on_image(self, image):
"""
Args:
image (np.ndarray): an image of shape (H, W, C) (in BGR order).
This is the format used by OpenCV.
Returns:
predictions (dict): the output of the model.
vis_output (VisImage): the visualized image output.
"""
vis_output = None
predictions = self.predictor(image)
# Convert image from OpenCV BGR format to Matplotlib RGB format.
image = image[:, :, ::-1]
visualizer = Visualizer(image,
self.metadata,
instance_mode=self.instance_mode)
if "instances" in predictions:
instances = predictions["instances"].to(self.cpu_device)
vis_output = visualizer.draw_instance_predictions(
predictions=instances)
return predictions, vis_output
def _frame_from_video(self, video):
while video.isOpened():
success, frame = video.read()
if success:
yield frame
else:
break
def run_on_video(self, video):
"""
Visualizes predictions on frames of the input video.
Args:
video (cv2.VideoCapture): a :class:`VideoCapture` object, whose source can be
either a webcam or a video file.
Yields:
ndarray: BGR visualizations of each video frame.
"""
video_visualizer_object = VideoVisualizer(self.metadata_object,
self.instance_mode)
video_visualizer_keypoint = VideoVisualizer(self.metadata_keypoint,
self.instance_mode)
def get_parameters(annos):
if annos["object_detection"]["pred_boxes"]:
temp = annos["object_detection"]["pred_boxes"][0]
obj_det = [1]
temp = np.asarray(temp)
temp = temp.flatten()
key_det = annos["keypoint_detection"]["pred_keypoints"][0]
key_det = np.asarray(key_det)
key_det = key_det[0:11, 0:2]
key_det = np.subtract(key_det, temp[0:2])
key_det = key_det.flatten()
else:
obj_det = [-1]
obj_det = np.asarray(obj_det)
key_det = annos["keypoint_detection"]["pred_keypoints"][0]
key_det = np.asarray(key_det)
key_det = key_det[0:11, 0:2]
key_det = key_det.flatten()
if annos["head_pose_estimation"]["predictions"]:
hp_est = annos["head_pose_estimation"]["predictions"][0]
hp_est = np.asarray(hp_est)
else:
hp_est = np.asarray([-100, -100, -100])
anno_list = np.concatenate((obj_det, key_det, hp_est))
return anno_list
def process_predictions(frame, predictions_object,
predictions_keypoint):
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
blank_image = np.zeros((frame.shape[0], frame.shape[1], 3),
np.uint8)
if "instances" in predictions_object:
predictions_object = predictions_object["instances"].to(
self.cpu_device)
self.data_json['object_detection'][
'pred_boxes'] = predictions_object.get(
'pred_boxes').tensor.numpy().tolist()
self.data_json['object_detection'][
'scores'] = predictions_object.get(
'scores').numpy().tolist()
vis_frame = video_visualizer_object.draw_instance_predictions(
frame, predictions_object)
if "instances" in predictions_keypoint:
predictions_keypoint = predictions_keypoint["instances"].to(
self.cpu_device)
self.data_json['keypoint_detection'][
'pred_boxes'] = predictions_keypoint.get(
'pred_boxes').tensor.numpy().tolist()
self.data_json['keypoint_detection'][
'scores'] = predictions_keypoint.get(
'scores').numpy().tolist()
self.data_json['keypoint_detection'][
'pred_keypoints'] = predictions_keypoint.get(
'pred_keypoints').numpy().tolist()
vis_frame = video_visualizer_keypoint.draw_instance_predictions(
vis_frame.get_image(), predictions_keypoint)
# head pose estimation
predictions, bounding_box, face_keypoints, w, face_area = head_pose_estimation(
frame, self.mtcnn, self.head_pose_module, self.transformations,
self.softmax, self.idx_tensor)
self.data_json['head_pose_estimation']['predictions'] = predictions
self.data_json['head_pose_estimation']['pred_boxes'] = bounding_box
# Converts Matplotlib RGB format to OpenCV BGR format
vis_frame = cv2.cvtColor(vis_frame.get_image(), cv2.COLOR_RGB2BGR)
for i in range(len(predictions)):
plot_pose_cube(vis_frame, predictions[i][0], predictions[i][1], predictions[i][2], \
tdx = (face_keypoints[i][0] + face_keypoints[i][2]) / 2, \
tdy= (face_keypoints[i][1] + face_keypoints[i][3]) / 2, \
size = w[i])
# draw_axis(vis_frame, predictions[i][0], predictions[i][1], predictions[i][2], \
# tdx = (face_keypoints[i][0] + face_keypoints[i][2]) / 2, \
# tdy= (face_keypoints[i][1] + face_keypoints[i][3]) / 2, \
# size = w[i])
data_json = self.data_json
self.data_json['frame'] = self.frame_count
self.frame_count += 1
inputs_MLP = get_parameters(self.data_json)
inputs_MLP = Variable(torch.from_numpy(inputs_MLP)).float().cuda()
outputs_MLP = self.mlp_model(inputs_MLP)
predicted_MLP = (outputs_MLP >= 0.5)
cv2.putText(vis_frame,str(predicted_MLP.item()), (10,700), \
cv2.FONT_HERSHEY_SIMPLEX, 3, (0,0,0), 10)
return vis_frame, data_json
frame_gen = self._frame_from_video(video)
for frame in frame_gen:
yield process_predictions(frame, self.predictor_object(frame),
self.predictor_keypoint(frame))
import torch
from torch.autograd import Variable
import numpy as np
from utils.utils import get_rainfall, get_solar_insolation, get_temperature
import os
import requests
from skimage import io
import shutil
import pandas as pd
app = Flask(__name__)
weights_pth = './nn_weight/mlp_weight.pth'
model = MLP(input_size=3, output_size=1)
model.load_state_dict(torch.load(weights_pth,
map_location=torch.device('cpu')))
model.eval()
def get_prediction(in_vector):
in_vector = torch.from_numpy(np.array(in_vector))
in_vector = Variable(in_vector).float()
outputs = model.forward(in_vector)
predicted = (outputs >= 0.755).float()
return predicted.cpu().numpy().tolist()[0]
@app.route('/predict', methods=['POST'])
def predict():
if request.method == 'POST':
data = request.json
